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We have developed an efficient stochastic AVA inversion technique that works directly in a fine-scale stratigraphic grid, and is conditioned by well data and multiple seismic angle stacks. We use a Bayesian framework and a linearized, weak contrast approximation of the Zoeppritz equation to construct a joint log-Gaussian posterior distribution for P-and S-wave impedances. We apply a Sequential Gaussian Simulation algorithm to sample the posterior PDF. We perform a trace-bytrace decomposition of the global posterior into local posterior distributions, conditioned by previously simulated traces. Trace-by-trace sampling of the local PDFs generates multiple, high-resolution realizations of the elastic properties. The new sequential algorithm has been implemented to take full advantage of parallel architectures and scales approximately linearly with the number of CPUs. The technique has been successfully tested using real data and a large layered model containing more than 30 × 10 6 grid-cells.

Static reservoir connectivity analysis is sometimes based on 3D facies or "geobody" models defined by combining well data and inverted seismic impedances. However, this is mostly performed from deterministic inversion results that provide limited information on uncertainty and may yield biased estimates of reservoir volume. Here, we present a workflow exploiting stochastic impedance realisations for facies characterisation and connectivity analysis with uncertainty, and illustrate the workflow using seismic and well data from a turbidite sand reservoir. After pre-stack stochastic inversion, we perform seismic facies classification based on cross-plots of inverted elastic attributes, calibrated to log data. Applying the classification to all impedance realisations, we generate multiple seismic sand facies models that can be averaged to produce a sand probability cube, reflecting the level of uncertainty in the inversion process. We then identify all connected sand "geobodies" in each realisation. We rank the facies realisations based on computed geobody statistics. Finally, we combine the geobodies from all realisations to calculate the probability of connection to given wells and the uncertainty in connected sand volume.

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International audienceEnsemble-based optimization methods are often efficiently applied to history-matching problems. Although satisfactory matches can be obtained, the updated realizations, affected by spurious correlations, generally fail to preserve prior information when using a small ensemble, even when localization is applied. In this work, we propose a multi-scale approach based on grid-adaptive second-generation wavelets. These wavelets can be applied on irregular reservoir grids of any dimensions containing dead or flat cells. The proposed method starts by modifying a few low frequency parameters (coarse scales) and then progressively allows more important updates on a limited number of sensitive parameters of higher resolution (fine scales). The Levenberg-Marquardt ensemble randomized maximum likelihood (LM-enRML) is used as optimization method with a new space-frequency distance-based localization of the Kalman gain, specifically designed for the multi-scale scheme. The algorithm is evaluated on two test cases. The first test is a 2D synthetic case in which several inversions are run using independent ensembles. The second test is the Brugge benchmark case with 10 years of history. The efficiency and quality of results of the multi-scale approach are compared with the grid-block-based LM-enRML with distance-based localization. We observe that the final realizations better preserve the spatial contrasts of the prior models and are less noisy than the realizations updated using a standard grid-block method, while matching the production data equally well